An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks
Open accessAbstract
Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76–357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1–20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.
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In VIOLearner, multi-scale projections help overcome gradient locality during training and also support online error correction at runtime.
“However, in our network, multi-scale projections not only help to overcome gradient locality during training (see [33], [47] for a broader discussion of this well-known issue), but also aid in online error correction at runtime.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [47]
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References 25 · Cited by 4
Research works printed but not linked: 6
- [20]Han S. Mao H. Dally W.J. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding arXiv 2015 1510.00149
- [21]Wen W. Wu C. Wang Y. Chen Y. Li H. Learning structured sparsity in deep neural networks Advances in Neural Information Processing Systems The MIT Press Cambridge, MA, USA 2016 2074 2082
- [22]Anwar S. Hwang K. Sung W. Structured pruning of deep convolutional neural networks arXiv 2015 1512.08571
- [23]Jaderberg M. Simonyan K. Zisserman A. Kavukcuoglu K. Spatial Transformer Networks arXiv 2015 1506.02025v1
- [24]Glorot X. Bordes A. Bengio Y. Deep Sparse Rectifier Neural Networks Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics Fort Lauderdale, FL, USA 11–13 April 2011 Volume 15 275
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- Authors
- Shamwell, E. J., Nothwang, W. D., & Perlis, D.
- Affiliations
- DEVCOM Army Research Laboratory (US)
- University of Maryland, College Park (US)
- Corresponding author
- Shamwell, E. Jared
- ORCID
- Shamwell, E. Jared
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2018-05-04
- Volume
- 18
- Issue
- 5
- Pages
- 1427
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC5982537
- OpenAlex
- W2801717995
- Cited by
- 4
- References
- 25
- Citations by year
- 2020: 2 · 2019: 1 · 2018: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Army Research Laboratory
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- visual motion estimation, multi-sensor fusion, unsupervised deep learning, heteroscedastic noise, size, weight, and power constraints, odometry
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